## Loading required package: ggplot2
## Loading required package: reshape2
# importing our data
data = data.import()
## Warning in if (is.na(value)) name = "NA" else if (value >= 348.75 || value
## <= : the condition has length > 1 and only the first element will be used
head(data)
Overview
summary(data)
## dtPosix dt
## Min. :2017-01-01 00:00:00 Min. :1.483e+09
## 1st Qu.:2017-04-01 10:45:00 1st Qu.:1.491e+09
## Median :2017-07-03 15:30:00 Median :1.499e+09
## Mean :2017-07-03 03:05:03 Mean :1.499e+09
## 3rd Qu.:2017-10-04 02:15:00 3rd Qu.:1.507e+09
## Max. :2017-12-31 23:00:00 Max. :1.515e+09
## NA's :37
## dt_iso city_id temp
## 2017-03-18 06:00:00 +0000 UTC: 3 Min. :2950159 Min. :263.1
## 2017-03-30 01:00:00 +0000 UTC: 3 1st Qu.:2950159 1st Qu.:277.1
## 2017-03-30 02:00:00 +0000 UTC: 3 Median :2950159 Median :283.1
## 2017-03-30 03:00:00 +0000 UTC: 3 Mean :2950159 Mean :283.4
## 2017-03-30 04:00:00 +0000 UTC: 3 3rd Qu.:2950159 3rd Qu.:289.1
## (Other) :9104 Max. :2950159 Max. :305.1
## NA's : 37 NA's :37 NA's :37
## temp_min temp_max pressure humidity
## Min. :261.1 Min. :263.7 Min. : 980 Min. : 14.00
## 1st Qu.:277.1 1st Qu.:277.4 1st Qu.:1010 1st Qu.: 67.00
## Median :283.1 Median :283.1 Median :1016 Median : 81.00
## Mean :283.0 Mean :283.7 Mean :1015 Mean : 77.17
## 3rd Qu.:289.1 3rd Qu.:289.1 3rd Qu.:1021 3rd Qu.: 93.00
## Max. :305.1 Max. :305.1 Max. :1043 Max. :100.00
## NA's :37 NA's :37 NA's :37 NA's :37
## wind_speed wind_deg rain_3h clouds_all
## Min. : 0.00 Min. : 0.0 Min. :0.118 Min. : 0.00
## 1st Qu.: 2.00 1st Qu.:120.0 1st Qu.:0.150 1st Qu.: 0.00
## Median : 3.00 Median :230.0 Median :0.380 Median : 75.00
## Mean : 3.43 Mean :197.5 Mean :0.672 Mean : 45.13
## 3rd Qu.: 5.00 3rd Qu.:270.0 3rd Qu.:0.889 3rd Qu.: 75.00
## Max. :14.00 Max. :360.0 Max. :9.865 Max. :100.00
## NA's :37 NA's :37 NA's :9066 NA's :37
## weather_id weather_main weather_description weather_icon
## Min. :200.0 Clouds :3434 Sky is Clear :2972 01n :1593
## 1st Qu.:701.0 Clear :2973 broken clouds :2380 04d :1390
## Median :800.0 Rain :1195 light rain : 803 01d :1379
## Mean :728.3 Mist : 598 mist : 598 04n :1171
## 3rd Qu.:803.0 Fog : 383 scattered clouds: 536 50n : 673
## Max. :804.0 (Other): 536 (Other) :1830 (Other):2913
## NA's :37 NA's : 37 NA's : 37 NA's : 37
## temp.c temp.c.group weekday hours
## Min. :-10.02 Min. :-10.00 sun :1331 4 : 392
## 1st Qu.: 4.00 1st Qu.: 5.00 wed :1316 6 : 392
## Median : 10.00 Median : 11.00 fri :1314 1 : 391
## Mean : 10.21 Mean : 10.24 thu :1303 3 : 390
## 3rd Qu.: 16.00 3rd Qu.: 17.00 tue :1290 2 : 389
## Max. : 32.00 Max. : 32.00 (Other):2565 (Other):7165
## NA's :37 NA's :1647 NA's : 37 NA's : 37
## chb.all chb.background chb.traffic cht.all
## Min. :0.2000 Min. :0.1000 Min. :0.200 Min. : 0.000
## 1st Qu.:0.6667 1st Qu.:0.5000 1st Qu.:0.750 1st Qu.: 1.333
## Median :0.9333 Median :0.7000 Median :1.050 Median : 2.000
## Mean :1.1730 Mean :0.9167 Mean :1.303 Mean : 2.483
## 3rd Qu.:1.3667 3rd Qu.:1.1000 3rd Qu.:1.550 3rd Qu.: 3.000
## Max. :8.9000 Max. :9.8000 Max. :9.400 Max. :19.667
## NA's :1 NA's :162 NA's :9 NA's :1
## cht.background cht.traffic co.all co.traffic
## Min. : 0.00 Min. : 0.000 Min. :0.1000 Min. :0.1000
## 1st Qu.: 1.00 1st Qu.: 1.500 1st Qu.:0.2667 1st Qu.:0.2667
## Median : 1.00 Median : 2.500 Median :0.3500 Median :0.3500
## Mean : 1.81 Mean : 2.819 Mean :0.3867 Mean :0.3867
## 3rd Qu.: 2.00 3rd Qu.: 3.500 3rd Qu.:0.4500 3rd Qu.:0.4500
## Max. :24.00 Max. :18.500 Max. :2.2500 Max. :2.2500
## NA's :161 NA's :4 NA's :1 NA's :1
## no2.all no2.background no2.traffic no2.suburb
## Min. : 3.929 Min. : 4.00 Min. : 4.00 Min. : 1.20
## 1st Qu.: 19.750 1st Qu.: 15.40 1st Qu.: 31.00 1st Qu.: 6.60
## Median : 27.625 Median : 22.40 Median : 44.83 Median :10.80
## Mean : 29.279 Mean : 25.35 Mean : 45.88 Mean :12.94
## 3rd Qu.: 36.806 3rd Qu.: 32.60 3rd Qu.: 58.33 3rd Qu.:17.00
## Max. :144.062 Max. :171.40 Max. :187.67 Max. :66.40
## NA's :1 NA's :1 NA's :1 NA's :1
## no.all no.background no.traffic no.suburb
## Min. : 0.4375 Min. : 0.000 Min. : 1.167 Min. : 0.000
## 1st Qu.: 6.4375 1st Qu.: 1.250 1st Qu.: 15.333 1st Qu.: 0.200
## Median : 13.0000 Median : 2.800 Median : 31.143 Median : 0.400
## Mean : 17.8855 Mean : 6.473 Mean : 40.297 Mean : 1.783
## 3rd Qu.: 23.0000 3rd Qu.: 6.000 3rd Qu.: 53.500 3rd Qu.: 1.200
## Max. :273.3125 Max. :361.800 Max. :419.667 Max. :90.000
## NA's :1 NA's :1 NA's :1 NA's :1
## nox.all nox.background nox.traffic nox.suburb
## Min. : 6.00 Min. : 5.20 Min. : 7.833 Min. : 1.60
## 1st Qu.: 30.69 1st Qu.: 18.20 1st Qu.: 55.333 1st Qu.: 7.00
## Median : 48.27 Median : 27.00 Median : 94.000 Median : 11.60
## Mean : 56.59 Mean : 35.22 Mean :107.422 Mean : 15.66
## 3rd Qu.: 71.00 3rd Qu.: 41.60 3rd Qu.:140.333 3rd Qu.: 19.25
## Max. :561.44 Max. :723.80 Max. :828.500 Max. :189.20
## NA's :1 NA's :1 NA's :1 NA's :1
## o3.all o3.background o3.traffic o3.suburb
## Min. : 0.50 Min. : 0.00 Min. : 1.0 Min. : 0.25
## 1st Qu.: 24.17 1st Qu.: 20.00 1st Qu.:11.0 1st Qu.: 25.62
## Median : 42.50 Median : 38.50 Median :25.0 Median : 45.00
## Mean : 44.07 Mean : 40.46 Mean :24.3 Mean : 45.94
## 3rd Qu.: 60.67 3rd Qu.: 57.50 3rd Qu.:35.0 3rd Qu.: 63.00
## Max. :142.67 Max. :138.00 Max. :72.0 Max. :145.00
## NA's :1 NA's :2 NA's :8469 NA's :1
## pm10.all pm10.background pm10.traffic pm10.suburb
## Min. : 4.00 Min. : 3.333 Min. : 5.00 Min. : 3.00
## 1st Qu.: 13.91 1st Qu.: 12.667 1st Qu.: 16.80 1st Qu.: 10.00
## Median : 18.82 Median : 17.667 Median : 22.40 Median : 14.00
## Mean : 22.70 Mean : 21.351 Mean : 26.68 Mean : 17.29
## 3rd Qu.: 27.70 3rd Qu.: 26.333 3rd Qu.: 32.20 3rd Qu.: 21.33
## Max. :283.73 Max. :211.667 Max. :465.40 Max. :106.33
## NA's :1 NA's :1 NA's :1 NA's :1
## so2.all so2.background so2.traffic wind.deg.name
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Length:9156
## 1st Qu.: 0.500 1st Qu.: 0.000 1st Qu.: 1.000 Class :character
## Median : 1.000 Median : 1.000 Median : 1.000 Mode :character
## Mean : 1.522 Mean : 1.198 Mean : 1.844
## 3rd Qu.: 1.500 3rd Qu.: 1.000 3rd Qu.: 2.000
## Max. :356.000 Max. :27.000 Max. :699.000
## NA's :4 NA's :27 NA's :54
pm10 over the year
pollutant = c("pm10.background", "pm10.traffic", "pm10.suburb")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")

plot.pollutant(data, pollutant, month = "01", yMax = 150)

plot.pollutant(data, pollutant, month = "02")

plot.pollutant(data, pollutant, month = "03")
## Warning: Removed 3 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "04")

plot.pollutant(data, pollutant, month = "05")

plot.pollutant(data, pollutant, month = "06")

plot.pollutant(data, pollutant, month = "07")

plot.pollutant(data, pollutant, month = "08")

plot.pollutant(data, pollutant, month = "09")

plot.pollutant(data, pollutant, month = "10")

plot.pollutant(data, pollutant, month = "11")

plot.pollutant(data, pollutant, month = "12")

chb over the year
pollutant = c("chb.background", "chb.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")

plot.pollutant(data, pollutant, month = "01")
## Warning: Removed 3 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "02")
## Warning: Removed 1 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "03")
## Warning: Removed 7 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "04")
## Warning: Removed 2 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "05")
## Warning: Removed 16 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "06")
## Warning: Removed 30 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "07")
## Warning: Removed 3 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "08")
## Warning: Removed 2 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "09")
## Warning: Removed 52 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "10")
## Warning: Removed 25 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "11")
## Warning: Removed 27 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "12")
## Warning: Removed 3 rows containing missing values (geom_point).

CHT over the year
pollutant = c("cht.background", "cht.traffic")
plot.pollutant(data, pollutant, month = "01")
## Warning: Removed 2 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "02")

plot.pollutant(data, pollutant, month = "03")
## Warning: Removed 4 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "04")
## Warning: Removed 3 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "05")
## Warning: Removed 14 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "06")
## Warning: Removed 28 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "07")
## Warning: Removed 3 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "08")
## Warning: Removed 2 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "09")
## Warning: Removed 53 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "10")
## Warning: Removed 30 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "11")
## Warning: Removed 26 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "12")

CO over the year
# there are only traffic sensors for co
pollutant = c("co.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")

plot.pollutant(data, pollutant, month = "01")

plot.pollutant(data, pollutant, month = "02")

plot.pollutant(data, pollutant, month = "03")
## Warning: Removed 1 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "04")

plot.pollutant(data, pollutant, month = "05")

plot.pollutant(data, pollutant, month = "06")

plot.pollutant(data, pollutant, month = "07")

plot.pollutant(data, pollutant, month = "08")

plot.pollutant(data, pollutant, month = "09")

plot.pollutant(data, pollutant, month = "10")

plot.pollutant(data, pollutant, month = "11")

plot.pollutant(data, pollutant, month = "12")

5. no2 over the year
pollutant = c("no2.background", "no2.traffic")
plot.pollutant(data, pollutant, month = "01", day = "01", title = "1. January with silvester firework peak")

plot.pollutant(data, pollutant, month = "01")

plot.pollutant(data, pollutant, month = "02")

plot.pollutant(data, pollutant, month = "03")
## Warning: Removed 2 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "04")

plot.pollutant(data, pollutant, month = "05")

plot.pollutant(data, pollutant, month = "06")

plot.pollutant(data, pollutant, month = "07")

plot.pollutant(data, pollutant, month = "08")

plot.pollutant(data, pollutant, month = "09")

plot.pollutant(data, pollutant, month = "10")

plot.pollutant(data, pollutant, month = "11")

plot.pollutant(data, pollutant, month = "12")

6. O3 over the year
pollutant = c("o3.background", "o3.traffic", "o3.suburb")
plot.pollutant(data, pollutant, month = "01")
## Warning: Removed 801 rows containing missing values (geom_point).

plot.pollutant(data, pollutant, month = "02")
## Warning: Removed 709 rows containing missing values (geom_point).
